An improved model and feature set for signature recognition

Automatic on-line signature recognition has been investigated by several authors in order to allow machines to recognize an user from its own biometric traits. The following paper deals with features and models required in order to allow a machine to learn and discriminate signatures. The proposed solution approaches the signature making process as the motion of a point in a bi-dimensional space and model s statistic properties of the motion via the well known Maximum a Posteriori training of Gaussian Mixture Models. Comparing our approach to state-of-the-art solutions, major advancements have been found. As first, both system accuracy in signature discrimination and system resistance to forgeries have been double. Eventually, the proposed modeling technique leads to smaller templates, whose size is halved with respect to state-of-theart alternatives.